Overview

Dataset statistics

Number of variables13
Number of observations6497
Missing cells38
Missing cells (%)< 0.1%
Duplicate rows983
Duplicate rows (%)15.1%
Total size in memory660.0 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Alerts

Dataset has 983 (15.1%) duplicate rowsDuplicates
fixed acidity is highly overall correlated with typeHigh correlation
volatile acidity is highly overall correlated with typeHigh correlation
residual sugar is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with residual sugar and 2 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
type is highly overall correlated with fixed acidity and 3 other fieldsHigh correlation
citric acid has 150 (2.3%) zerosZeros

Reproduction

Analysis started2023-05-16 06:09:59.158256
Analysis finished2023-05-16 06:10:18.535117
Duration19.38 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
white
4898 
red
1599 

Length

Max length5
Median length5
Mean length4.5077728
Min length3

Characters and Unicode

Total characters29287
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Length

2023-05-16T09:10:18.622617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-16T09:10:18.807293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
white 4898
75.4%
red 1599
 
24.6%

Most occurring characters

ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29287
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 29287
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6497
22.2%
w 4898
16.7%
h 4898
16.7%
i 4898
16.7%
t 4898
16.7%
r 1599
 
5.5%
d 1599
 
5.5%

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)1.6%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.2165793
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:18.973714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2967499
Coefficient of variation (CV)0.17969038
Kurtosis5.057727
Mean7.2165793
Median Absolute Deviation (MAD)0.6
Skewness1.7228045
Sum46813.95
Variance1.6815602
MonotonicityNot monotonic
2023-05-16T09:10:19.113010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 354
 
5.4%
6.6 326
 
5.0%
6.4 305
 
4.7%
7 282
 
4.3%
6.9 279
 
4.3%
7.2 273
 
4.2%
6.7 264
 
4.1%
7.1 257
 
4.0%
6.5 242
 
3.7%
7.4 238
 
3.7%
Other values (96) 3667
56.4%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
< 0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.1%
4.9 8
 
0.1%
5 30
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 2
< 0.1%
15 2
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct187
Distinct (%)2.9%
Missing8
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.33969102
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:19.247929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.16464903
Coefficient of variation (CV)0.48470234
Kurtosis2.8270813
Mean0.33969102
Median Absolute Deviation (MAD)0.08
Skewness1.4955116
Sum2204.255
Variance0.027109303
MonotonicityNot monotonic
2023-05-16T09:10:19.378635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 286
 
4.4%
0.24 265
 
4.1%
0.26 255
 
3.9%
0.25 238
 
3.7%
0.22 235
 
3.6%
0.27 232
 
3.6%
0.23 221
 
3.4%
0.2 217
 
3.3%
0.3 214
 
3.3%
0.32 205
 
3.2%
Other values (177) 4121
63.4%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 6
 
0.1%
0.11 13
 
0.2%
0.115 3
 
< 0.1%
0.12 37
0.6%
0.125 2
 
< 0.1%
0.13 44
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

Distinct89
Distinct (%)1.4%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.3187219
Minimum0
Maximum1.66
Zeros150
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:19.717327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1452648
Coefficient of variation (CV)0.45577289
Kurtosis2.4015821
Mean0.3187219
Median Absolute Deviation (MAD)0.07
Skewness0.47303243
Sum2069.78
Variance0.021101862
MonotonicityNot monotonic
2023-05-16T09:10:19.850634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 337
 
5.2%
0.28 301
 
4.6%
0.32 289
 
4.4%
0.49 283
 
4.4%
0.26 257
 
4.0%
0.34 249
 
3.8%
0.29 244
 
3.8%
0.27 236
 
3.6%
0.24 232
 
3.6%
0.31 229
 
3.5%
Other values (79) 3837
59.1%
ValueCountFrequency (%)
0 150
2.3%
0.01 40
 
0.6%
0.02 56
 
0.9%
0.03 32
 
0.5%
0.04 41
 
0.6%
0.05 25
 
0.4%
0.06 30
 
0.5%
0.07 33
 
0.5%
0.08 37
 
0.6%
0.09 42
 
0.6%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 2
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 2
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct316
Distinct (%)4.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.4443264
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:19.993593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.7581247
Coefficient of variation (CV)0.87396023
Kurtosis4.3581344
Mean5.4443264
Median Absolute Deviation (MAD)1.7
Skewness1.4349998
Sum35360.9
Variance22.639751
MonotonicityNot monotonic
2023-05-16T09:10:20.130076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 235
 
3.6%
1.8 228
 
3.5%
1.6 223
 
3.4%
1.4 219
 
3.4%
1.2 195
 
3.0%
2.2 187
 
2.9%
2.1 179
 
2.8%
1.9 176
 
2.7%
1.7 175
 
2.7%
1.5 171
 
2.6%
Other values (306) 4507
69.4%
ValueCountFrequency (%)
0.6 2
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.4%
0.9 41
 
0.6%
0.95 4
 
0.1%
1 93
1.4%
1.05 1
 
< 0.1%
1.1 146
2.2%
1.15 3
 
< 0.1%
1.2 195
3.0%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 2
< 0.1%
26.05 2
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 2
< 0.1%
20.8 2
< 0.1%
20.7 2
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

Distinct214
Distinct (%)3.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.05604157
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:20.273227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.035036025
Coefficient of variation (CV)0.62517922
Kurtosis50.894874
Mean0.05604157
Median Absolute Deviation (MAD)0.011
Skewness5.3998488
Sum363.99
Variance0.0012275231
MonotonicityNot monotonic
2023-05-16T09:10:20.405896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 206
 
3.2%
0.036 200
 
3.1%
0.042 187
 
2.9%
0.046 185
 
2.8%
0.05 182
 
2.8%
0.04 182
 
2.8%
0.048 182
 
2.8%
0.047 175
 
2.7%
0.045 174
 
2.7%
0.034 169
 
2.6%
Other values (204) 4653
71.6%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 4
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 10
0.2%
0.019 9
0.1%
0.02 16
0.2%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
< 0.1%
0.414 2
< 0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.525319
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:20.545118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.7494
Coefficient of variation (CV)0.58146483
Kurtosis7.9062381
Mean30.525319
Median Absolute Deviation (MAD)12
Skewness1.2200661
Sum198323
Variance315.04119
MonotonicityNot monotonic
2023-05-16T09:10:20.674426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 183
 
2.8%
6 170
 
2.6%
26 161
 
2.5%
15 157
 
2.4%
24 152
 
2.3%
31 152
 
2.3%
17 149
 
2.3%
34 146
 
2.2%
35 144
 
2.2%
23 142
 
2.2%
Other values (125) 4941
76.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 59
 
0.9%
4 52
 
0.8%
5 129
2.0%
5.5 1
 
< 0.1%
6 170
2.6%
7 96
1.5%
8 91
1.4%
9 91
1.4%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.74457
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:20.839179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.521855
Coefficient of variation (CV)0.48833265
Kurtosis-0.37166365
Mean115.74457
Median Absolute Deviation (MAD)39
Skewness-0.0011774782
Sum751992.5
Variance3194.72
MonotonicityNot monotonic
2023-05-16T09:10:20.993136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 72
 
1.1%
113 65
 
1.0%
117 57
 
0.9%
122 57
 
0.9%
124 56
 
0.9%
128 56
 
0.9%
114 56
 
0.9%
98 56
 
0.9%
118 55
 
0.8%
119 54
 
0.8%
Other values (266) 5913
91.0%
ValueCountFrequency (%)
6 3
 
< 0.1%
7 4
 
0.1%
8 14
 
0.2%
9 15
0.2%
10 28
0.4%
11 26
0.4%
12 29
0.4%
13 28
0.4%
14 33
0.5%
15 35
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99469663
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:21.127104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673
Coefficient of variation (CV)0.0030146609
Kurtosis6.606067
Mean0.99469663
Median Absolute Deviation (MAD)0.00231
Skewness0.50360173
Sum6462.544
Variance8.9920398 × 10-6
MonotonicityNot monotonic
2023-05-16T09:10:21.268449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 69
 
1.1%
0.9976 69
 
1.1%
0.992 64
 
1.0%
0.998 64
 
1.0%
0.9928 63
 
1.0%
0.9986 61
 
0.9%
0.9966 59
 
0.9%
0.9962 59
 
0.9%
0.9956 55
 
0.8%
0.9968 55
 
0.8%
Other values (988) 5879
90.5%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 2
< 0.1%
ValueCountFrequency (%)
1.03898 1
 
< 0.1%
1.0103 2
< 0.1%
1.00369 2
< 0.1%
1.0032 1
 
< 0.1%
1.00315 3
< 0.1%
1.00295 2
< 0.1%
1.00289 1
 
< 0.1%
1.0026 2
< 0.1%
1.00242 2
< 0.1%
1.00241 1
 
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)1.7%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.2183955
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:21.405918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.16074831
Coefficient of variation (CV)0.049946722
Kurtosis0.37006811
Mean3.2183955
Median Absolute Deviation (MAD)0.11
Skewness0.38696593
Sum20880.95
Variance0.025840018
MonotonicityNot monotonic
2023-05-16T09:10:21.551414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 200
 
3.1%
3.14 193
 
3.0%
3.22 185
 
2.8%
3.2 176
 
2.7%
3.15 170
 
2.6%
3.19 170
 
2.6%
3.18 168
 
2.6%
3.24 160
 
2.5%
3.12 154
 
2.4%
3.1 154
 
2.4%
Other values (98) 4758
73.2%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 3
 
< 0.1%
2.8 3
 
< 0.1%
2.82 1
 
< 0.1%
2.83 4
 
0.1%
2.84 1
 
< 0.1%
2.85 9
0.1%
2.86 10
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.53121515
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:21.691793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14881412
Coefficient of variation (CV)0.28013907
Kurtosis8.6598922
Mean0.53121515
Median Absolute Deviation (MAD)0.08
Skewness1.798467
Sum3449.18
Variance0.022145643
MonotonicityNot monotonic
2023-05-16T09:10:21.826513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 275
 
4.2%
0.46 243
 
3.7%
0.54 234
 
3.6%
0.44 232
 
3.6%
0.38 214
 
3.3%
0.48 208
 
3.2%
0.52 203
 
3.1%
0.49 197
 
3.0%
0.47 191
 
2.9%
0.45 190
 
2.9%
Other values (101) 4306
66.3%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 4
 
0.1%
0.27 13
 
0.2%
0.28 13
 
0.2%
0.29 16
 
0.2%
0.3 31
0.5%
0.31 35
0.5%
0.32 54
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
< 0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.491801
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:21.970161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.1927117
Coefficient of variation (CV)0.11368037
Kurtosis-0.53168738
Mean10.491801
Median Absolute Deviation (MAD)0.9
Skewness0.56571773
Sum68165.23
Variance1.4225613
MonotonicityNot monotonic
2023-05-16T09:10:22.110420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 367
 
5.6%
9.4 332
 
5.1%
9.2 271
 
4.2%
10 229
 
3.5%
10.5 227
 
3.5%
11 217
 
3.3%
9 215
 
3.3%
9.8 214
 
3.3%
10.4 194
 
3.0%
9.3 193
 
3.0%
Other values (101) 4038
62.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 5
 
0.1%
8.5 10
 
0.2%
8.6 23
 
0.4%
8.7 80
 
1.2%
8.8 109
1.7%
8.9 95
1.5%
9 215
3.3%
9.05 1
 
< 0.1%
9.1 167
2.6%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 12
0.2%
13.9 3
 
< 0.1%
13.8 2
 
< 0.1%
13.7 7
0.1%
13.6 13
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8183777
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2023-05-16T09:10:22.229583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87325527
Coefficient of variation (CV)0.1500857
Kurtosis0.23232227
Mean5.8183777
Median Absolute Deviation (MAD)1
Skewness0.18962269
Sum37802
Variance0.76257477
MonotonicityNot monotonic
2023-05-16T09:10:22.312813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2836
43.7%
5 2138
32.9%
7 1079
 
16.6%
4 216
 
3.3%
8 193
 
3.0%
3 30
 
0.5%
9 5
 
0.1%
ValueCountFrequency (%)
3 30
 
0.5%
4 216
 
3.3%
5 2138
32.9%
6 2836
43.7%
7 1079
 
16.6%
8 193
 
3.0%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 193
 
3.0%
7 1079
 
16.6%
6 2836
43.7%
5 2138
32.9%
4 216
 
3.3%
3 30
 
0.5%

Interactions

2023-05-16T09:10:16.514067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.076798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.673607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.116835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.722282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.182846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.620042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.197853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.573501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.975186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.555892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.074325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.632822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.191152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.793807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.240779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.848699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.298409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.739258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.312520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.691722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.096226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.689375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.198404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.754894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.492801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.916068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.363816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.974768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.428660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.888552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.428876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.812503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.218457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.818744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.324457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.870106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.628675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.032400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.482561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.094693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.550806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.130913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.549536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.929562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.342781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.940372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.446418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.991231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.757316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.156351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.730364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.210893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.674378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.246783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.660150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.048084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.457307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.064949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.565849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.109716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.874825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.275763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.855981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.329952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.790292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.363831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.777569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.162765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.576985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.191093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.686091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.224766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:00.990926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.390927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:03.983580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.446053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.908684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.483194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.891748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.282055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.693436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.323060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.808130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.338928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.096493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.508374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.090927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.573200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.015952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.590299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.999103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.384728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.805141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.436811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:15.919435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.455732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.214997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.624268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.211308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.690872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.134266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.708075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.106879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.499830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:12.923627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.570491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.031260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.576419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.328698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.740826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.334237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.812571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.252635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.828307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.223529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.616656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.032203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.696314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.152879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.694664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.445771image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.869258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.464709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:05.937739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.373281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:08.956348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.344977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.737745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.318455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.823931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.277686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:17.818989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:01.563008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:02.995480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:04.596927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:06.062510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:07.500410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:09.077442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:10.463111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:11.857435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:13.439019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:14.951428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-05-16T09:10:16.400997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-05-16T09:10:22.421494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
fixed acidity1.0000.2020.270-0.0330.357-0.261-0.2340.434-0.2480.221-0.111-0.0990.505
volatile acidity0.2021.000-0.295-0.0660.416-0.367-0.3440.2610.1940.255-0.024-0.2580.664
citric acid0.270-0.2951.0000.075-0.0740.1220.1590.066-0.2850.0380.0200.1060.424
residual sugar-0.033-0.0660.0751.000-0.0360.3880.4550.527-0.229-0.138-0.330-0.0170.350
chlorides0.3570.416-0.074-0.0361.000-0.260-0.2680.5910.1640.370-0.401-0.2950.765
free sulfur dioxide-0.261-0.3670.1220.388-0.2601.0000.7410.006-0.164-0.221-0.1860.0870.419
total sulfur dioxide-0.234-0.3440.1590.455-0.2680.7411.0000.062-0.242-0.256-0.309-0.0550.800
density0.4340.2610.0660.5270.5910.0060.0621.0000.0120.275-0.699-0.3230.322
pH-0.2480.194-0.285-0.2290.164-0.164-0.2420.0121.0000.2530.1400.0320.332
sulphates0.2210.2550.038-0.1380.370-0.221-0.2560.2750.2531.0000.0040.0300.471
alcohol-0.111-0.0240.020-0.330-0.401-0.186-0.309-0.6990.1400.0041.0000.4470.147
quality-0.099-0.2580.106-0.017-0.2950.087-0.055-0.3230.0320.0300.4471.0000.130
type0.5050.6640.4240.3500.7650.4190.8000.3220.3320.4710.1470.1301.000

Missing values

2023-05-16T09:10:17.999019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-16T09:10:18.245214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-16T09:10:18.435693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0white7.00.270.3620.70.04545.0170.01.00103.000.458.86
1white6.30.300.341.60.04914.0132.00.99403.300.499.56
2white8.10.280.406.90.05030.097.00.99513.260.4410.16
3white7.20.230.328.50.05847.0186.00.99563.190.409.96
4white7.20.230.328.50.05847.0186.00.99563.190.409.96
5white8.10.280.406.90.05030.097.00.99513.260.4410.16
6white6.20.320.167.00.04530.0136.00.99493.180.479.66
7white7.00.270.3620.70.04545.0170.01.00103.000.458.86
8white6.30.300.341.60.04914.0132.00.99403.300.499.56
9white8.10.220.431.50.04428.0129.00.99383.220.4511.06
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
6487red6.60.7250.207.80.07329.079.00.997703.290.549.25
6488red6.30.5500.151.80.07726.035.00.993143.320.8211.66
6489red5.40.7400.091.70.08916.026.00.994023.670.5611.66
6490red6.30.5100.132.30.07629.040.00.995743.420.7511.06
6491red6.80.6200.081.90.06828.038.00.996513.420.829.56
6492red6.20.6000.082.00.09032.044.00.994903.450.5810.55
6493red5.90.5500.102.20.06239.051.00.995123.52NaN11.26
6494red6.30.5100.132.30.07629.040.00.995743.420.7511.06
6495red5.90.6450.122.00.07532.044.00.995473.570.7110.25
6496red6.00.3100.473.60.06718.042.00.995493.390.6611.06

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
640white7.00.150.2814.70.05129.0149.00.997922.960.399.078
772white7.30.190.2713.90.05745.0155.00.998072.940.418.888
553white6.80.180.3012.80.06219.0171.00.998083.000.529.077
803white7.40.160.3013.70.05633.0168.00.998252.900.448.777
802white7.40.160.2715.50.05025.0135.00.998402.900.438.776
806white7.40.190.3012.80.05348.5229.00.998603.140.499.176
807white7.40.190.3114.50.04539.0193.00.998603.100.509.266
854white7.60.200.3014.20.05653.0212.50.999003.140.468.986
248white5.70.220.2016.00.04441.0113.00.998623.220.468.965
330white6.20.230.3617.20.03937.0130.00.999463.230.438.865